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This study aims to explore whether and how task characteristics affect employee agility in the context of enterprise social media (ESM).

Adopting the social network ties perspective, this study examines how task characteristics (i.e. task complexity, task interdependence and task non-routineness) affect employee agility by promoting their social network ties (i.e. instrumental ties and expressive ties) and how ESM visibility moderates their relationships. Data gathered from 341 ESM users in workplaces were analyzed using Smart-PLS 3.2.

First, task complexity, task interdependence and task non-routineness have positive effects on instrumental and expressive ties, which in turn influences agility; Second, instrumental ties have a stronger effect on employee agility relative to expressive ties; Finally, ESM visibility positively moderates the effects of task complexity and task non-routineness on social network ties.

The findings provide guidance for organizational managers on how to use task characteristics and ESM to improve employee agility, as well as insights for social media designers to optimize ESM functions to improve agility.

This study provides empirical evidence to explain the roles of task characteristics and social network ties in influencing employee agility, thus clarifying the inconsistent findings in extant research. The moderating effects of ESM visibility on the relationships between task characteristics and social network ties are also examined, thus providing further insights on the positive role of ESM in organizations.

The purpose of this paper is to provide a fast and accurate network for spatiotemporal action localization in videos. It detects human actions both in time and space simultaneously in real-time, which is applicable in real-world scenarios such as safety monitoring and collaborative assembly.

This paper design an end-to-end deep learning network called collaborator only watch once (COWO). COWO recognizes the ongoing human activities in real-time with enhanced accuracy. COWO inherits from the architecture of you only watch once (YOWO), known to be the best performing network for online action localization to date, but with three major structural modifications: COWO enhances the intraclass compactness and enlarges the interclass separability in the feature level. A new correlation channel fusion and attention mechanism are designed based on the Pearson correlation coefficient. Accordingly, a correction loss function is designed. This function minimizes the same class distance and enhances the intraclass compactness. Use a probabilistic K-means clustering technique for selecting the initial seed points. The idea behind this is that the initial distance between cluster centers should be as considerable as possible. CIOU regression loss function is applied instead of the Smooth L1 loss function to help the model converge stably.

COWO outperforms the original YOWO with improvements of frame mAP 3% and 2.1% at a speed of 35.12 fps. Compared with the two-stream, T-CNN, C3D, the improvement is about 5% and 14.5% when applied to J-HMDB-21, UCF101-24 and AGOT data sets.

COWO extends more flexibility for assembly scenarios as it perceives spatiotemporal human actions in real-time. It contributes to many real-world scenarios such as safety monitoring and collaborative assembly.